Recurrent context layered radial basis function neural network for the identification of nonlinear dynamical systems-Reference-Cited by-同舟云学术

Recurrent context layered radial basis function neural network for the identification of nonlinear dynamical systems

Published:2024-05 Issue: Volume:580 Page:127524
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Kumar Rajesh^ORCID

Publisher

Elsevier BV

Reference36 articles.

1. A new nonlinear formulation-based prediction approach using artificial neural network (ANN) model for rubberized cement composite;Jalal;Eng. Comput.,2022

2. New delay dependent stability criteria for recurrent neural networks with interval time-varying delay;Yang;ISA Trans.,2014

3. Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems;Han;ISA Trans.,2014

4. On the adaptation of recurrent neural networks for system identification;Forgione;Automatica,2023

5. Physics-informed recurrent neural network modeling for predictive control of nonlinear processes;Zheng;J. Process Control,2023

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